>
> Maybe you can use techniques to reduce the number of input
variables. An example is PCA (principal components analysis).
> If you don't know about this, I suggest you to read some simple
tutorial. I can't find anyone I've read, but you can search for
something on Google. Try that ones without many formulas, just to
know about the technique.
> If you think this can help you, you can use the software Minitab.
If you want some help using the software, you can tell me.
>
>
>
>
> ---------------------------------
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Thanks Renato,
I have found a software to make a PCA analysis of my data.
The result is like this. I have reduced the volume of data, in fact
I have 13 Principal Components (PC) and about 80 inputs.
Explained Variance (Eigenvalues):
Value PC 1 PC 2 PC 3 . . .
Eigenvalue 29.405 8.662 6.792 . . .
% of Var. 34.594 10.191 7.991 . . .
Cum. % 34.594 44.785 52.776 . . .
Component Loadings
(correlations between initial variables and principal components):
Variable PC 1 PC 2 PC 3
INPUT1 -0.009 0.038 0.029
INPUT2 0.039 0.061 0.608
INPUT3 0.041 0.072 0.620
INPUT4 -0.066 0.017 0.559
Component Score Coefficients (Eigenvectors):
Variable PC 1 PC 2 PC 3
INPUT1 -0.002 0.013 0.011
INPUT2 0.007 0.021 0.233
INPUT3 0.008 0.024 0.238
INPUT4 -0.012 0.006 0.215
Can I use this data to know an area of the total space where the
probability to find "1" as an output is maximum?
Thanks again.
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